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Use of Histopathological Image Analysis in Diagnostics
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Use of Histopathological Image Analysis in Diagnostics

A special issue of Diagnostics (ISSN 2075-4418). This special issue belongs to the section "Machine Learning and Artificial Intelligence in Diagnostics".

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1888

Special Issue Editors

Dr. Elizabeth Soilleux

E-Mail Website
Guest Editor
1. Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK
2. Haematopathology and Oncology Diagnostic Service, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
3. Department of Histopathology, Cambridge University Hospitals NHS Foundation Trust, Cambridge CB2 0QQ, UK
Interests: lymphoma; coeliac/celiac disease; T-cell receptor; B-cell receptor; T-cell receptor repertoire; B-cell receptor repertoire; clonality; molecular diagnostics; sequencing; immunohistochemistry; immunostaining; in situ hybridisation; digital image analysis; artificial intelligence; machine learning; autopsy; COVID-19; SARS-CoV-2; immunity
Special Issues, Collections and Topics in MDPI journals
Dr. Florian Jaeckle

E-Mail Website
Guest Editor Assistant
Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK
Interests: use of histopathological image analysis in diagnostics

Special Issue Information

Dear Colleagues,

This Special Issue, entitled "Use of Histopathological Image Analysis in Diagnostics," delves into the cutting-edge applications of histopathological image analysis in the realm of medical diagnosis. It explores how advanced computational techniques and artificial intelligence algorithms are transforming the way pathologists interpret tissue samples. By enhancing the precision and reproducibility of diagnostic assessments, these innovations are crucial in identifying diseases such as cancer, inflammation, and infections with unprecedented accuracy. The Special Issue showcases research that underscores the importance of integrating digital pathology into routine diagnostic workflows, ultimately improving patient care and outcomes.

Dr. Elizabeth Soilleux
Guest Editor

Dr. Florian Jaeckle
Guest Editor Assistant

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Diagnostics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • histopathological
  • diagnostics
  • artificial intelligence
  • algorithms
  • image analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

19 pages, 865 KiB  
Article
Duodenal Biopsy Audit: Relative Frequency of Diagnoses, Key Words on Request Forms Indicating Severe Pathology, and Potential Diagnoses for Intraepithelial Lymphocytosis, as a Foundation for Developing Artificial Intelligence Diagnostic Approaches
by Vrinda Shenoy, Jessica L. James, Amelia B. Williams-Walker, Nasyen P. R. Madhan Mohan, Kim N. Luu Hoang, Josephine Williams, Florian Jaeckle, Shelley C. Evans and Elizabeth J. Soilleux
Diagnostics 2025, 15(12), 1483; https://doi.org/10.3390/diagnostics15121483 - 11 Jun 2025
Viewed by 73
Abstract
Background/Objectives: Understanding the diagnostic landscape is essential prior to developing artificial intelligence (AI)-based diagnostic strategies for automating the diagnosis of duodenal biopsies. This study aims to (1) determine the frequencies of different diagnoses seen in endoscopic duodenal biopsies in a large, tertiary referral [...] Read more.
Background/Objectives: Understanding the diagnostic landscape is essential prior to developing artificial intelligence (AI)-based diagnostic strategies for automating the diagnosis of duodenal biopsies. This study aims to (1) determine the frequencies of different diagnoses seen in endoscopic duodenal biopsies in a large, tertiary referral centre; (2) identify key words on histopathology request forms that could indicate that a biopsy may contain a serious pathology and should not be diagnosed by an AI system; and (3) investigate the proportion of cases described as showing “intraepithelial lymphocytosis” that might be coeliac disease. Methods: To achieve this, we audited 18 months’ worth of duodenal biopsy reports in our centre. Results: A total of 6245 duodenal biopsies were identified, of which 73.76% were normal and at least 8.84% fell within the spectrum of coeliac disease. Additionally, 6.47% were classified as showing non-specific inflammation, 1.86% were adenomas, 0.45% were carcinomas, 0.06% were neuroendocrine tumours, 0.10% were lymphomas, and 0.03% were cases of flat dysplasia, giving a total of 0.64% of dysplastic or malignant diagnoses. Rarer diagnoses included ulceration, Helicobacter pylori infection, giardiasis, lymphangiectasia, transplant rejection, and lymphoma. Furthermore, 227 biopsies (3.63%) showed isolated intraepithelial lymphocytosis, of which 33 cases (14.5%) gave an overall clinicopathological picture of coeliac disease. Conclusions: We present the first long-term audit of all endoscopic duodenal biopsies received by the histopathology department of a tertiary-care facility. The results indicate that a fully automated diagnostic histopathology reporting system able to identify normal duodenal biopsies and biopsies within the spectrum of coeliac disease-associated enteropathy could decrease pathologists’ endoscopic duodenal biopsy workload by up to 80%. Full article
(This article belongs to the Special Issue Use of Histopathological Image Analysis in Diagnostics)
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15 pages, 3204 KiB  
Article
Vision Transformers for Low-Quality Histopathological Images: A Case Study on Squamous Cell Carcinoma Margin Classification
by So-yun Park, Gelan Ayana, Beshatu Debela Wako, Kwangcheol Casey Jeong, Soon-Do Yoon and Se-woon Choe
Diagnostics 2025, 15(3), 260; https://doi.org/10.3390/diagnostics15030260 - 23 Jan 2025
Viewed by 1479
Abstract
Background/Objectives: Squamous cell carcinoma (SCC), a prevalent form of skin cancer, presents diagnostic challenges, particularly in resource-limited settings with a low-quality imaging infrastructure. The accurate classification of SCC margins is essential to guide effective surgical interventions and reduce recurrence rates. This study proposes [...] Read more.
Background/Objectives: Squamous cell carcinoma (SCC), a prevalent form of skin cancer, presents diagnostic challenges, particularly in resource-limited settings with a low-quality imaging infrastructure. The accurate classification of SCC margins is essential to guide effective surgical interventions and reduce recurrence rates. This study proposes a vision transformer (ViT)-based model to improve SCC margin classification by addressing the limitations of convolutional neural networks (CNNs) in analyzing low-quality histopathological images. Methods: This study introduced a transfer learning approach using a ViT architecture customized with additional flattening, batch normalization, and dense layers to enhance its capability for SCC margin classification. A performance evaluation was conducted using machine learning metrics averaged over five-fold cross-validation and comparisons were made with the leading CNN models. Ablation studies have explored the effects of architectural configuration on model performance. Results: The ViT-based model achieved superior SCC margin classification with 0.928 ± 0.027 accuracy and 0.927 ± 0.028 AUC, surpassing the highest performing CNN model, InceptionV3 (accuracy: 0.86 ± 0.049; AUC: 0.837 ± 0.029), demonstrating robustness of ViT over CNN for low-quality histopathological images. Ablation studies have reinforced the importance of tailored architectural configurations for enhancing diagnostic performance. Conclusions: This study underscores the transformative potential of ViTs in histopathological analysis, especially in resource-limited settings. By enhancing diagnostic accuracy and reducing dependence on high-quality imaging and specialized expertise, it presents a scalable solution for global cancer diagnostics. Future research should prioritize optimizing ViTs for such environments and broadening their clinical applications. Full article
(This article belongs to the Special Issue Use of Histopathological Image Analysis in Diagnostics)
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